*result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }

  TFE_OpReset(reused_op.get(), "Identity", custom_device_name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_EQ(tensorflow::string(TFE_OpGetDevice(reused_op.get(), status.get())),
            tensorflow::string(custom_device_name));
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_OpReset(reused_op.get(), "Identity",

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/dlpack.h"

#include <string>

#include "include/dlpack/dlpack.h"  // from @dlpack
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"

void TF_InitGraph(TP_OptimizerRegistrationParams* params, TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed
// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `num_values` and `storage_size`, they
// will be used in `TF_GetNodesToPreserveList`.

    initiates CI/CD tests.
-   We can't move forward if these tests fail.
-   In such situations, we may request you to make further changes to your PR
    for the tests to pass.
-   Once the tests pass, we now bring all the code into the internal code base,
    using a job called "copybara".

**5. Copy to Google Internal codebase and run internal CI**

-   Once the PR is in the Google codebase, we make sure it integrates well with

  virtual const FunctionDef* FindFunctionDef(const string& name) const = 0;

  // Find and return a function record added by its name.
  virtual core::RefCountPtr<FunctionRecord> FindRecord(
      const string& name) const = 0;

  // Return the ParsedName of Host CPU device.
  virtual const DeviceNameUtils::ParsedName& HostCPUParsedName() const = 0;
  virtual const string& HostCPUName() const = 0;

  // the new one and remove the file from cache.
  bool ValidateAndUpdateFileSignature(const std::string& filename,
                                      int64_t file_signature)
      ABSL_LOCKS_EXCLUDED(mu_);

  /// Remove all cached blocks for `filename`.
  void RemoveFile(const std::string& filename) ABSL_LOCKS_EXCLUDED(mu_);

  /// Remove all cached data.
  void Flush() ABSL_LOCKS_EXCLUDED(mu_);

}

static std::vector<std::string> GetChildrenBounded(
    GCSFile* gcs_file, std::string dir, uint64_t max_results, bool recursive,
    bool include_self_directory_marker, TF_Status* status) {
  std::string bucket, prefix;
  MaybeAppendSlash(&dir);
  ParseGCSPath(dir, true, &bucket, &prefix, status);

  std::vector<std::string> result;
  uint64_t count = 0;
  std::string delimiter = recursive ? "" : "/";

                       subdiv_offsets.size());

  return op;
}

TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, op_name.c_str(), status);

  EXPECT_EQ(TF_GetCode(status), TF_OK);

  ServerDef actual;
  ASSERT_TRUE(actual.ParseFromArray(result->data, result->length));
  string actual_text_proto;
  tensorflow::protobuf::TextFormat::PrintToString(actual, &actual_text_proto);
  EXPECT_EQ(expected_text_proto, actual_text_proto);

  const string malformed_text_proto(R"(cluster {
  job {
    name: "worker")");
  TF_Buffer* null_result =